A case based reasoning approach for the monitoring of business workflows

Authors:
Stelios Kapetanakis;Miltos Petridis;Brian Knight;Jixin Ma;Liz Bacon
Affiliations:
School of Computing and Mathematical Sciences, University of Greenwich, Maritime Greenwich Campus, Old Royal Naval College, Park Row, Greenwich, London, UK;School of Computing and Mathematical Sciences, University of Greenwich, Maritime Greenwich Campus, Old Royal Naval College, Park Row, Greenwich, London, UK;School of Computing and Mathematical Sciences, University of Greenwich, Maritime Greenwich Campus, Old Royal Naval College, Park Row, Greenwich, London, UK;School of Computing and Mathematical Sciences, University of Greenwich, Maritime Greenwich Campus, Old Royal Naval College, Park Row, Greenwich, London, UK;School of Computing and Mathematical Sciences, University of Greenwich, Maritime Greenwich Campus, Old Royal Naval College, Park Row, Greenwich, London, UK
Venue:
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Year:
2010

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Abstract

This paper presents an approach for the intelligent diagnosis and monitoring of business workflows based on operation data in the form of temporal log data. The representation of workflow related case knowledge in this research using graphs is explained. The workflow process is orchestrated by a software system using BPEL technologies within a service-oriented architecture. Workflow cases are represented in terms of events and their corresponding temporal relationships. The matching and CBR retrieval mechanisms used in this research are explained and the architecture of an integrated intelligent monitoring system is shown. The paper contains an evaluation of the approach based on experiments on real data from a university quality assurance exam moderation system. The experiments and the evaluation of the approach is presented and is shown that a graph matching based similarity measure is capable to diagnose problems within business workflows. Finally, further work on the system and the extension to a full intelligent monitoring and process optimisation system is presented.